Overview

This article suggests how to install NVIDIA GPU driver, CUDA Toolkit, NVIDIA Container Toolkit and other NVIDIA software from NVIDIA repository on NVIDIA GPU EC2 instances running AL2023 (Amazon Linux 2023)

Note that by using this method, you agree to NVIDIA Driver License Agreement, End User License Agreement and other related license agreement. If you are doing development, you may want to register for NVIDIA Developer Program.

This article applies to AL2023 only. Similar articles are available for AL2, Ubuntu Linux, RHEL/Rocky Linux and Windows.

This article install NVIDIA Tesla driver which does not support G6f instances with fractional GPUs. Refer to this article about NVIDIA GRID driver install.

Other Options

If you need AMIs preconfigured with NVIDIA GPU driver, CUDA, other NVIDIA software, and optionally PyTorch or TensorFlow framework, consider AWS Deep Learning AMIs. Refer to Release notes for DLAMIs for currently supported options, and Deep Learning graphical desktop on Amazon Linux 2023 (AL2023) with AWS Deep Learning AMI (DLAMI) for graphical desktop setup guidance.

Refer to NVIDIA drivers for your Amazon EC2 instance for NVIDIA driver install options and NVIDIA Driver Installation Guide for Tesla driver installation instructions.

For container workloads, consider Amazon ECS-optimized Linux AMIs and Amazon EKS optimized AMIs

Note: instructions in this article are not applicable to pre-built AMIs.

Custom ECS/EKS GPU-optimized AMI

If you wish to build your own custom Amazon ECS or EKS GPU-optimized AMI, install NVIDIA driver, Docker and NVIDIA container toolkit, and refer to How do I create and use custom AMIs in Amazon ECS? or How do I create custom Amazon Linux AMIs for Amazon EKS?

About CUDA toolkit

CUDA Toolkit is generally optional when GPU instance is used to run applications (as opposed to develop applications) as the CUDA application typically packages (by statically or dynamically linking against) the CUDA runtime and libraries needed.

Version support

CUDA version 12.5 and higher supports Amazon Linux 2023 package manager installation on x86_64.

CUDA version 12.9 and NVIDIA driver 570.148.08 adds arm64 support.

NVIDIA driver versions 560 to 575 from NVIDIA repository supports compute only / headless mode but not desktop mode.

Prerequisites

Go to Service Quotas console of your desired Region to verify On-Demand Instance quota value of your desired instance type:

• G instance types: Running On-Demand G and VT instances
• P instance types: Running On-Demand P instances

Request quota increase if the assigned value is less than vCPU count of your desired EC2 instance size. Do not proceed until your applied quota value is equal or higher than your instance type vCPU count

Prepare Amazon Linux 2023

Launch a new NVIDIA GPU instance running Amazon Linux 2023 preferably with at least 20 GB storage, either kernel-6.1 or kernel-6.12.

Connect to the instance as ec2-user

Update OS

Update OS

sudo dnf update -y

Optional: you may want to upgrade to latest release version (if available) and disable deterministic upgrade

sudo dnf upgrade --releasever=latest
echo latest | sudo tee /etc/dnf/vars/releasever

Restart your EC2 instance

sudo reboot

Install DKMS and kernel headers

sudo dnf clean all
sudo dnf install -y dkms 
sudo systemctl enable --now dkms
if (uname -r | grep -q ^6\\.12\\.); then
  if ( dnf search kernel6.12-headers | grep -q kernel ); then
    sudo dnf install -y kernel6.12-headers-$(uname -r) kernel6.12-devel-$(uname -r) kernel6.12-modules-extra-$(uname -r) kernel6.12-modules-extra-common-$(uname -r) --allowerasing
  else  
    sudo dnf install -y kernel-headers-$(uname -r) kernel-devel-$(uname -r) kernel6.12-modules-extra-$(uname -r) kernel-modules-extra-common-$(uname -r) --allowerasing
  fi
else
  if ( ! cat /etc/dnf/dnf.conf | grep ^exclude | grep -q 6\\.12 ); then
    sudo sed -i '$aexclude=kernel6.12* kernel-headers-6.12* kernel-devel-6.12* kernel-modules-extra-common-6.12* kernel-modules-extra-6.12*' /etc/dnf/dnf.conf
  fi  
  sudo dnf install -y kernel-headers-$(uname -r) kernel-devel-$(uname -r) kernel-modules-extra-$(uname -r) kernel-modules-extra-common-$(uname -r)
fi

For AL2023 running kernel 6.1, the script blocks any inadvertent upgrade to kernel 6.12.

Add repository

You can choose either NVIDIA or AL2023 repository

Option 1: NVIDIA repo

if (arch | grep -q x86); then
  ARCH=x86_64
else
  ARCH=sbsa
fi
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/amzn2023/$ARCH/cuda-amzn2023.repo
sudo dnf clean expire-cache

If you are installing from AWS China Region, you may be able to change repository source from https://developer.download.nvidia.com to https://developer.download.nvidia.cn

if (ec2-metadata -z | grep cn-); then
  sudo sed -i "s/nvidia\.com/nvidia\.cn/g" /etc/yum.repos.d/cuda-amzn2023.repo
  sudo dnf clean expire-cache
fi

Option 2: AL2023 repo (x86_64 only)

nvidia-release was added to 2023.6.20241031 release and enables a yum repository with NVIDIA drivers.

sudo dnf install -y nvidia-release

Install NVIDIA driver

Option 1: NVIDIA repo

To install latest Tesla driver

sudo dnf module enable -y nvidia-driver:open-dkms
sudo dnf install -y nvidia-open 
sudo dnf install -y nvidia-xconfig

To install a specific driver branch, e.g. R570 production

sudo dnf module enable -y nvidia-driver:570-open
sudo dnf install -y nvidia-open
sudo dnf install -y nvidia-xconfig

The above install open-source GPU kernel module which is recommended by NVIDIA (and is different from Nouveau open-source driver). Refer to Driver Installation Guide about NVIDIA Kernel Modules and installation options.

Option 2: AL2023 repo (x86_64 only)

sudo dnf install -y nvidia-open
sudo dnf install -y nvidia-xconfig

Verify

nvidia-smi

Output should be similar to below

Sat Aug  9 01:17:25 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.65.06              Driver Version: 580.65.06      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:1E.0 Off |                    0 |
| N/A   31C    P8             10W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Optional: Install CUDA toolkit

To install latest CUDA Toolkit

sudo dnf install -y cuda-toolkit

To install a specific series, e.g. 12.x

sudo dnf install -y cuda-toolkit-12

To install a specific version, e.g. 12.9

sudo dnf install -y cuda-toolkit-12-9

Refer to CUDA documentation for installation options

Verify

/usr/local/cuda/bin/nvcc -V

Output should be similar to below

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2025 NVIDIA Corporation
Built on Wed_Jul_16_07:30:01_PM_PDT_2025
Cuda compilation tools, release 13.0, V13.0.48
Build cuda_13.0.r13.0/compiler.36260728_0

Post-installation Actions

Refer to NVIDIA CUDA Installation Guide for Linux for post-installation actions before CUDA Toolkit can be used. For example, you may want to modify your PATH environment variable to include /usr/local/cuda/bin.

sed -i '$aexport PATH=\"\$PATH:/usr/local/cuda/bin\"' /home/ec2-user/.bashrc
. /home/ec2-user/.bashrc

Optional: NVIDIA Container Toolkit

NVIDIA Container toolkit supports AL2023 on both x86_64 and arm64.

For arm64, use g5g.2xlarge or larger instance size as g5g.xlarge may cause failures due to the limited system memory.

if (! dnf search nvidia | grep -q nvidia-container-toolkit); then
  sudo dnf config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
fi
sudo dnf install -y nvidia-container-toolkit

Refer to NVIDIA Container toolkit documentation about supported platforms, prerequisites and installation options

Verify Container Toolkit

nvidia-container-cli -V

Output should be similar to below

cli-version: 1.17.8
lib-version: 1.17.8
build date: 2025-05-30T13:47+0000
build revision: 6eda4d76c8c5f8fc174e4abca83e513fb4dd63b0
build compiler: gcc 4.8.5 20150623 (Red Hat 4.8.5-44)
build platform: x86_64
build flags: -D_GNU_SOURCE -D_FORTIFY_SOURCE=2 -DNDEBUG -std=gnu11 -O2 -g -fdata-sections -ffunction-sections -fplan9-extensions -fstack-protector -fno-strict-aliasing -fvisibility=hidden -Wall -Wextra -Wcast-align -Wpointer-arith -Wmissing-prototypes -Wnonnull -Wwrite-strings -Wlogical-op -Wformat=2 -Wmissing-format-attribute -Winit-self -Wshadow -Wstrict-prototypes -Wunreachable-code -Wconversion -Wsign-conversion -Wno-unknown-warning-option -Wno-format-extra-args -Wno-gnu-alignof-expression -Wl,-zrelro -Wl,-znow -Wl,-zdefs -Wl,--gc-sections

Container engine configuration

Refer to NVIDIA Container Toolkit site for container engine configuration instructions.

Install and configure Docker

To install and configure docker

sudo dnf install -y docker
sudo systemctl enable docker
sudo usermod -aG docker ec2-user

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Verify Docker engine configuration

To verify docker configuration

sudo docker run --rm --runtime=nvidia --gpus all public.ecr.aws/amazonlinux/amazonlinux:2023 nvidia-smi

Output should be similar to below

Unable to find image 'public.ecr.aws/amazonlinux/amazonlinux:2023' locally
2023: Pulling from amazonlinux/amazonlinux
38a4201225fe: Pull complete 
Digest: sha256:b605bd9526950f8d77a79b11667e4e7c75683e9d7dc6bb148bc023b8503163cb
Status: Downloaded newer image for public.ecr.aws/amazonlinux/amazonlinux:2023
Fri Aug  8 17:18:54 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.65.06              Driver Version: 580.65.06      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:1E.0 Off |                    0 |
| N/A   28C    P8             13W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Install on EC2 instance at launch

To install NVIDIA driver and NVIDIA Container Toolkit including docker when launching a new AL2023 GPU instance preferably with latest patches applied and with at least 20 GB storage, you can use the following as user data script.

Remove the # characters (except the first line) if you wish to install CUDA toolkit

#!/bin/bash
sudo dnf clean all
sudo dnf install -y dkms
sudo systemctl enable dkms
if (uname -r | grep -q ^6\\.12\\.); then
  if ( dnf search kernel6.12-headers | grep -q kernel ); then
    sudo dnf install -y kernel6.12-headers-$(uname -r) kernel6.12-devel-$(uname -r) kernel6.12-modules-extra-$(uname -r) kernel6.12-modules-extra-common-$(uname -r) --allowerasing
  else  
    sudo dnf install -y kernel-headers-$(uname -r) kernel-devel-$(uname -r) kernel6.12-modules-extra-$(uname -r) kernel-modules-extra-common-$(uname -r) --allowerasing
  fi
else
  if ( ! cat /etc/dnf/dnf.conf | grep ^exclude | grep -q 6\\.12 ); then
    sudo sed -i '$aexclude=kernel6.12* kernel-headers-6.12* kernel-devel-6.12* kernel-modules-extra-common-6.12* kernel-modules-extra-6.12*' /etc/dnf/dnf.conf
  fi  
  sudo dnf install -y kernel-headers-$(uname -r) kernel-devel-$(uname -r) kernel-modules-extra-$(uname -r) kernel-modules-extra-common-$(uname -r)
fi

cd /tmp

if (arch | grep -q x86); then
  ARCH=x86_64
else
  ARCH=sbsa
fi
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/amzn2023/$ARCH/cuda-amzn2023.repo
sudo dnf clean expire-cache

sudo dnf module enable -y nvidia-driver:open-dkms
sudo dnf install -y nvidia-open
sudo dnf install -y nvidia-xconfig

# sudo dnf install -y cuda-toolkit
# sed -i '$aexport PATH=\"\$PATH:/usr/local/cuda/bin\"' /home/ec2-user/.bashrc
# . /home/ec2-user/.bashrc

sudo dnf install -y docker
sudo systemctl enable docker
sudo usermod -aG docker ec2-user

if (! dnf search nvidia | grep -q nvidia-container-toolkit); then
  sudo dnf config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
fi
sudo dnf install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

sudo reboot

Verify

Connect to your EC2 instance

nvidia-smi
/usr/local/cuda/bin/nvcc -V
nvidia-container-cli -V
sudo docker run --rm --runtime=nvidia --gpus all public.ecr.aws/amazonlinux/amazonlinux:2023 nvidia-smi

View /var/log/cloud-init-output.log to troubleshoot any installation issues.

Perform post-installation actions in order to use CUDA toolkit. To verify integrity of installation, you can download, compile and run CUDA samples such as deviceQuery.

If Docker and NVIDIA container toolkit (but not CUDA toolkit) are installed and configured, you can use CUDA samples container image to validate CUDA driver.

sudo docker run --rm --runtime=nvidia --gpus all nvcr.io/nvidia/k8s/cuda-sample:devicequery

GUI (graphical desktop) remote access

If you need remote graphical desktop access, refer to How do I install GUI (graphical desktop) on Amazon EC2 instances running Amazon Linux 2023 (AL2023)?

This article installs NVIDIA Tesla driver (also know as NVIDIA Datacenter Driver), which is intended primarily for GPU compute workloads. If configured in xorg.conf, Tesla drivers support one display of up to 2560x1600 resolution.

GRID drivers provide access to four 4K displays per GPU and are certified to provide optimal performance for professional visualization applications. Refer to NVIDIA drivers for your Amazon EC2 instance and GPU-accelerated graphical desktop on Amazon Linux 2023 (AL2023) with NVIDIA GRID and Amazon DCV for setup options.

Upgrading to Kernel 6.12

If your AL2023 with NVIDIA driver is running kernel 6.1, you can consider updating to kernel 6.12 for improvements in scheduling, networking, security, and system tracing.

Unblock kernel 6.12 update

sudo sed -i '/exclude=kernel6.12/d' /etc/dnf/dnf.conf

Refer to Updating an AL2023 instance to kernel 6.12 for update instructions.

Other Software

DCGM (Data Center GPU Manager)

To install DCGM

CUDA_VERSION=$(nvidia-smi | sed -E -n 's/.*CUDA Version: ([0-9]+)[.].*/\1/p')
sudo dnf install --assumeyes \
                   --setopt=install_weak_deps=True \
                   datacenter-gpu-manager-4-cuda${CUDA_VERSION}

Refer to DCGM documentation for more information

Verify

dcgmi --version

Output should be similar to below

dcgmi  version: 4.4.1

GDS (GPUDirect Storage)

To install NVIDIA Magnum IO GPUDirect® Storage (GDS)

sudo dnf install -y nvidia-gds

To install for a specific CUDA version, e.g. 13.0

sudo dnf install -y nvidia-gds-13-0

Reboot

Reboot after installation is complete

sudo reboot

Verify

To verify module

lsmod | grep nvidia_fs

Output should be similar to below

nvidia_fs             262144  0
nvidia              11481088  3 nvidia_uvm,nvidia_fs,nvidia_modeset

To verify successful installation

/usr/local/cuda/gds/tools/gdscheck -p

Output should be similar to below

 GDS release version: 1.15.1.6
 nvidia_fs version:  2.26 libcufile version: 2.12
 Platform: x86_64
...
...
 =========                    
 GPU INFO:                                                                                       
 =========       
 GPU index 0 NVIDIA A10G bar:1 bar size (MiB):32768 supports GDS, IOMMU State: Disabled          
 ==============
 PLATFORM INFO:
 ==============       
 IOMMU: disabled      
 Nvidia Driver Info Status: Supported(Nvidia Open Driver Installed)                              
 Cuda Driver Version Installed:  13000
 Platform: g5.xlarge, Arch: x86_64(Linux 6.12.40-63.114.amzn2023.x86_64)                         
 Platform verification succeeded 

Refer to GDS documentation and Driver installation guide for more information

UFM (Unified Fabric Manager)

P6 instance requires additional configuration as per EC2 and NVIDIA documentation.

To install latest NVIDIA Unified Fabric Manager (UFM)

sudo dnf install -y nvidia-fabricmanager
sudo systemctl enable nvidia-fabricmanager

To install specific branch, e.g. 580

sudo dnf install -y nvidia-fabricmanager-580
sudo systemctl enable nvidia-fabricmanager

Restart your EC2 instance

sudo reboot

Verify

nv-fabricmanager -v
systemctl status nvidia-fabricmanager

Output should be similar to below

Fabric Manager version is : 580.95.05

● nvidia-fabricmanager.service - NVIDIA fabric manager service
     Loaded: loaded (/usr/lib/systemd/system/nvidia-fabricmanager.service; enabled; preset: enabled)
     Active: active (running) since ......... UTC; 1min 4s ago
    Process: 22851 ExecStart=/usr/bin/nvidia-fabricmanager-start.sh --mode start (code=exited, status=0/SUCCESS)
   Main PID: 22881 (nv-fabricmanage)
      Tasks: 18 (limit: 3355442)
     Memory: 38.1M
        CPU: 633ms
     CGroup: /system.slice/nvidia-fabricmanager.service
             └─22881 /usr/bin/nv-fabricmanager -c /usr/share/nvidia/nvswitch/fabricmanager.cfg
.........compute.internal nv-fabricmanager[22881]: Starting nvidia-fabricmanager.service - NVIDIA fabric manager service...
.........compute.internal nv-fabricmanager[22881]: Detected Pre-NVL5 system
.........compute.internal nv-fabricmanager[22881]: Connected to 1 node.
.........compute.internal nv-fabricmanager[22881]: Successfully configured all the available NVSwitches to route GPU NVLink traffic. NVLink Peer-to-Peer support will be enabled once the GPUs are successfully registered with the NVLink fabric.
.........compute.internal nv-fabricmanager[22881]: Started "Nvidia Fabric Manager"
.........compute.internal nv-fabricmanager[22881]: Started nvidia-fabricmanager.service - NVIDIA fabric manager service.

To view GPU fabric registration status

nvidia-smi -q -i 0 | grep -i -A 2 Fabric

Output should be similar to below after the GPU has been successfully registered

    Fabric
        State                             : Completed
        Status                            : Success

Refer to Fabric Manager documentation for supported platforms, and any additional installation or configuration steps